This paper presents a multispectral-image-based model for eliminating the shadow effect on NDVI. This NDVI Shadow-Effect-Eliminating model (NSEE) is derived from simulated data and then applied on two Landsat 8 OLI images (one for the experiment and one for verification).NDVI plays a key role in the multispectral remote sensing retrieval of vegetation and has been widely used in many areas. However, the shadow normally existing in remote sensing images always influences the accuracy of NDVI. These effects will be transmitted in the process of further remote sensing retrieval, thereby resulting in errors. In this case, eliminating the shadow effect on vegetation is crucial and has a positive application value and high necessity.The difference between the shaded region and non-shaded regions in an image depends on how much solar irradiance these regions have received (i.e., supposing that the shaded region receives a solar diffuse radiation whereas the bright region receives total solar radiation, including direct and diffuse ones). The total solar irradiance (E₀), solar direct irradiance (E_d), and solar diffuse irradiance (E_f) are simulated by using MODTRAN 4.0, the typical vegetation reflectance spectra (R) are selected from the spectra library in ENVI 5.3, and the radiances of vegetations (L_R, L_R′) in the shaded and non-shaded regions are calculated (using E₀, E_f, and R. The mechanism behind the shadow effects on the NDVI of vegetation is analyzed by using the aforementioned simulated data. A normalized dark pixel index (NDPI) that shows high sensitivity in shadow detection and low relativity to NDVI is introduced. By analyzing the relationships between two sets of simulated NDVI (under solar diffuse radiation and under total solar radiation) of the same vegetation spectrum (to simulate shaded and non-shaded situations in remote sensing image), the NSEE model of NDVI Shadow-Effect-Eliminating(NSEE) is constructed to correct the NDVI in shaded regions based on the NDVI in the bright regions of an image.The NSEE model is applied on two Landsat 8 OLI images. The results show that, the NDVI values in the shaded regions are basically corrected to be normal, whereas the NDVIs in the bright regions remain stable in both the experimental and verification images. The NSEE model can also normalize the skewness of the NDVI statistical histogram caused by the shadow effect. The NDVI values of the experimental and verification images are compared pixel by pixel along the two transect lines, and the result shows that the reduction in NDVI due to shadow is eliminated and that the NDVI in the bright region belonging to either vegetation or non-vegetation pixels remains stable. The total RMSE is 0.067, thereby validating the effectiveness of the model is effective.The NSEE model effectively eliminates the shadow effects of shadow on the NDVI of vegetations. This model can also distinguish the NDVI-decreasing pixels of NDVI-decreasing (due to shadow effects) from those pixels with relatively low original NDVI values, thereby suggesting that the model fits well with land type. This model is entirely based on the image information itself, it can effectively maintain the relative spatial relations of NDVI, and effectively eliminate the influence of shadow. The proposed NESS model is based on a physical mechanism, it is concise and can be easily applied. This model only depends on the information of the multispectral image, does not require different data sources, and shows a convenient and efficient calculation.

remote sensing;shaded vegetation canopy;NDVI;NDPI;shadow effects;Landsat 8 OLI;multispectral remote sensing